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You can use AI more safely by treating every prompt as a disclosure: check whether the information is allowed in the exact tool and account you plan to use, remove details the task does not need, and review what happens to prompts and responses after submission. If your organization’s rules do not approve the workflow, do not paste the material in—even if you have changed names or the service says it does not use prompts for training.
The same provider may apply different protections across products, plans, features, and connected agents. “Not used to train” also does not necessarily mean “not stored.” The safest approach is to assess the information, the complete workflow, and the output—not just the AI brand or a privacy label.
What counts as sensitive or confidential information?
Information is sensitive or confidential when its use or disclosure is restricted by law, policy, contract, a person’s expectations, or the circumstances in which it was obtained. Your organization’s classification scheme is the authority to follow; labels such as “internal” or “confidential” do not have one universal meaning across employers or jurisdictions.
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Before prompting, consider whether the material includes personal information, account or payment details, credentials, health or financial information, customer records, internal plans, unpublished work, proprietary text, or information subject to a contract or government restriction. A seemingly ordinary detail can also identify someone when combined with other details—for example, a job role, small location, and exact date.
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UK Government guidance for civil servants says not to enter sensitive information or personal data into the tools covered by that guidance. That is advice for a specific audience and set of tools, not a universal legal rule. The Office of the Privacy Commissioner of Canada advises that sensitive or confidential information should be entered only where authorized. Follow the rules that apply to you and your information.
Use this pre-prompt decision process
Make the decision before opening the chat or attaching a file. If you cannot establish that a particular tool and configuration are approved for the information, treat that as a reason not to submit it.
- Classify the source. Decide whether it is public, internal, confidential, regulated, personal, or subject to contractual or other sharing restrictions. Check your access rights as well as the owner’s restrictions: being able to view a document does not automatically mean you may send it to an AI service.
- Define the task. Identify what the model needs to do—such as explain a public policy, organize non-sensitive notes, or improve the wording of a generic message. Separate that goal from the source material. Often the task can be completed without submitting the original document or identifying details.
- Check authorization and the exact workflow. Confirm that your organization permits the information in that specific service, account, product, plan, and configuration. Include connected agents, plugins, web search, or other features that may receive prompt-derived data.
- Reduce the input. Remove information not needed for the task, de-identify what remains where feasible, and use a public or synthetic example if it will work. Do not treat anonymization as proof that re-identification is impossible.
- Review the result before using or sharing it. Check accuracy and bias, and look for confidential information repeated or inferred in the answer. Apply your normal review and approval process before acting on or distributing the output.
If the policy is unclear, ask the responsible security, privacy, or legal contact before submitting the material. A sanitized prompt does not automatically make an unapproved workflow acceptable.
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Give the model the least revealing input that can still solve the problem. A prompt can disclose information even when it is not published in a final answer: the service may process or retain the interaction, and connected features may have their own handling terms.
Remove details the task does not need
- Replace a person’s name with a role such as “customer” or “project lead.”
- Remove account numbers, email addresses, phone numbers, credentials, and other direct identifiers.
- Generalize exact dates and locations if the precise values are not necessary.
- Share only the relevant excerpt rather than a full email thread, contract, report, or customer file.
- Remove unique facts that could identify a person or organization when combined, even if names are absent.
- Do not include passwords, access tokens, private keys, or other secrets as a way to troubleshoot a problem.
Use a safer substitute when possible
For a writing or analysis task, try a synthetic example that preserves the structure but uses invented details. For research, use information that is already public. For a document summary, consider whether you can ask about a general rule or provide a short, cleared excerpt instead of the complete document.
De-identification reduces disclosure; it is not a guarantee of anonymity. If a combination of details could let someone infer a person, customer, project, or organization, remove or generalize more—or do not use the material unless the workflow is authorized.
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Check the exact service, account, and settings
Do not rely on a provider’s general privacy statement or the word “enterprise.” Confirm the terms that apply to the exact product and account you will use. A provider can offer different handling for different subscriptions, features, and customer configurations.
| What to check | Question to answer |
|---|---|
| Account and eligibility | Is this an organization-approved account? Does the stated protection cover this user, product, plan, and use case? |
| Retention | Are prompts and responses stored? For how long? Who can set or manage retention, and how are deletion requests handled? |
| Training and secondary use | Are prompts, responses, or retrieved organizational data used for model training or another purpose? Check the terms for this service and configuration. |
| Access controls | Does the AI retrieve organizational information according to existing permissions? Are the underlying document permissions correct? |
| Connected features | Can agents, plugins, web search, or external services receive the prompt or a derived query? Which terms govern those services? |
| Exceptions and access | What legal, safety, abuse-monitoring, or operational exceptions apply, and who may access data in those circumstances? |
| Administration and incident handling | Which settings can administrators control, and what is the approved process if information is submitted by mistake? |
For example, Microsoft says protections vary by subscription. Its documentation describes interaction-history storage and administrator-managed retention while stating that those interactions are not used to train foundation models. It also describes distinct handling for web queries and advises reviewing the terms and privacy notices for agents. These details are specific to the Microsoft products and configurations covered by its documentation; check the applicable terms rather than assuming they apply to every product or feature.
OpenAI describes Zero Data Retention for eligible API customers. That is a specific arrangement with an eligibility scope, not a general guarantee for consumer services or every account. The provider’s announcement also notes an exception involving flagged potential child sexual abuse material images. Read the actual terms for the eligible service and account before relying on a retention commitment.
Understand the difference between training and retention
“Not used to train models” answers a question about one possible use of data; it does not, by itself, answer whether the service stores the interaction, for how long, or who can access it. A service can avoid using interactions to train foundation models while still keeping interaction history under a retention policy.
Likewise, a retention commitment does not automatically answer whether a connected agent or external feature receives data, whether administrators can access records, or what exceptions apply. Evaluate these questions separately. The practical goal is not to find a broad label such as “private,” but to understand the specific data flows and controls in the workflow you intend to use.
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A chat may do more than send a prompt to one model. A connected agent, plugin, web-search feature, or other service may receive all or part of the prompt, a derived query, or information retrieved from organizational sources. Check the terms and privacy notice for each connected component and confirm whether your organization has approved it.
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Permission-aware retrieval can reduce exposure to information a user is not allowed to see, but it is not a substitute for correct permissions. Microsoft states that Copilot can surface organizational data users have permission to view. If a source document is available too broadly, an AI feature that respects those permissions can still surface it to people who should not have access. Ask the document owner or administrator to correct source permissions when necessary.
Organizational protections can help, but they do not override classification rules, legal duties, contractual restrictions, or configuration mistakes. Use protected workflows only when you are authorized to use them for the information in question.
Choose lower-risk tasks—and verify the output
Public-information research and summaries of publicly available material are examples of lower-risk uses in UK Government guidance. They avoid exposing restricted source material, though the resulting answer still needs review. AI output can be inaccurate, biased, or misleading; verify facts against reliable sources and follow the applicable citation and approval practices before sharing it.
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Compare services by data flow, not by marketing labels
When choosing between tools, write down the complete path your information will take. Start with the account and eligibility: an organizational login is not proof that every product or feature is approved. Confirm which subscription commitment applies to the named user, workspace, API project, and model.
Next compare retention in operational terms. Ask whether prompts, uploaded files, generated responses, logs, and feedback records are stored; the retention period; who can change it; and whether administrators can retrieve or delete records. Then examine training and secondary use separately. A commitment about foundation-model training may not cover abuse monitoring, service improvement, support access, or a connected provider.
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Check permissions at the source. If a tool retrieves company documents, verify that folders, shared links, and inherited permissions are correct before enabling retrieval. Review web-search behavior as well: a private prompt may become a derived query sent to a search provider. Agents and plugins deserve their own review because their notices and contracts may differ from the host chat.
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What to do if you submitted restricted information
If sensitive information went into a tool that may not be approved, use your organization’s incident process promptly. Do not assume that deleting a conversation removes every stored copy or resolves the exposure; retention and deletion behavior depends on the service and configuration.
- Stop adding related information to the same conversation or connected workflow.
- Record what was submitted, when, through which account and product, and whether files, agents, or web search were involved. Keep the record factual and share it only through approved channels.
- Notify the responsible security, privacy, or legal contact and follow their instructions. The incident may require assessment under organizational policy, contract, or applicable law.
- Use available deletion or retention controls only as directed, and do not represent deletion as complete unless the service’s terms and the responsible team confirm what it covers.
- Review the workflow that led to the submission and adjust the approval checks, permissions, or user guidance to prevent a repeat.
A practical example: summarizing an internal report
Suppose you need a short summary of a report containing customer names, precise dates, account details, and an unpublished project plan. First, check the report’s classification and sharing restrictions. Then determine whether the specific AI product, account, and connected features are approved for that material. If they are not, do not upload the report.
If an approved workflow exists, consider whether the task can be done with a cleared excerpt or a version that removes names, account details, exact locations, and unnecessary dates. Check whether the model will retrieve additional internal documents or send derived queries to web search. After generating a summary, verify its claims against the authorized source and remove any confidential details that should not appear in the output. If the task can be completed with public information or a synthetic report, use that instead.
Common assumptions that create risk
- “I removed the name, so it is anonymous.” Other details can identify someone when combined. De-identification is a risk reduction, not a guarantee.
- “The account is for work, so it is approved.” Approval depends on the product, plan, configuration, information class, and task—not just whether the account is organizational.
- “The service does not train on my data, so nothing is kept.” Training and retention are separate questions; check both.
- “The AI can see only what I can see, so our data is safe.” That depends on correctly configured source permissions and the connected workflow.
- “A short prompt cannot disclose much.” A single identifier, secret, or proprietary passage can be enough to create a disclosure.
- “A correct-looking answer is safe to share.” Outputs can be misleading or repeat sensitive information; review them before relying on or distributing them.
Threats extend beyond storage
Privacy risks do not end with ordinary prompt retention. The Office of the Privacy Commissioner of Canada identifies prompt injection, model inversion, and jailbreaking among AI-related threats that call for ongoing awareness and mitigation. These concerns reinforce the need to assess the full workflow, restrict access appropriately, and avoid treating a provider’s data-use commitment as a complete security assessment.
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Do not place sensitive material in a prompt merely to test whether the model will reveal it, resist an attack, or keep a secret. Security testing should follow an authorized process with suitable test data and controls.
Frequently Asked Questions
Can I use a consumer AI chatbot for confidential work if I delete the chat afterward?
Not necessarily. Deletion may not remove every stored copy, and the service’s retention, logging, backup, and connected-feature practices depend on the exact product and configuration. Use the tool only if your organization authorizes that workflow and its terms meet your requirements.
Does de-identifying a document make it safe to upload?
De-identification lowers disclosure risk but does not guarantee anonymity. Combinations of dates, locations, roles, and unusual facts can still identify a person or project. Remove more detail or obtain authorization when re-identification remains plausible.
Is an enterprise or work account automatically approved for sensitive data?
No. Approval depends on the specific product, subscription, account configuration, information class, task, and connected features. Confirm the applicable policy and provider terms.
What is the difference between no training and no retention?
No training means the interaction is excluded from a particular model-training use. No retention means the service does not keep the data under a defined arrangement. A service may promise the first while storing interaction history, so verify both claims separately.
Can an AI with permission-aware access still expose confidential information?
It can surface information that the requesting user is already permitted to view, but incorrect source permissions can make confidential material broadly available. Permission-aware retrieval does not fix an incorrectly shared document or an unapproved connected service.
Are public-information summaries safe?
They are generally lower-risk because they avoid submitting restricted source material, and UK Government guidance gives them as an example. Still verify accuracy and bias, check citations, and inspect the output before sharing it.
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What should I do after pasting restricted information by mistake?
Stop the conversation, record what was submitted and which account or features were involved, notify your security, privacy, or legal contact, and follow the incident process. Use deletion controls only as directed; do not assume deletion is complete.
The Bottom Line
Treat every prompt as a disclosure. Classify the information, confirm the exact account and workflow are authorized, minimize and de-identify what you send, review connected features and retention terms, and verify every output before using or sharing it. “Not used for training” is not the same as “not stored,” and no privacy label replaces sound classification and approval.
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